assignmentutor™您的专属作业导师

assignmentutor-lab™ 为您的留学生涯保驾护航 在代写统计与机器学习Statistical and Machine Learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写统计与机器学习Statistical and Machine Learning方面经验极为丰富，各种代写机器学习Statistical and Machine Learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|Event-based Sampling

In many business situations, the target that we are trying to model is rare. For example, the churn rate for a telecommunications company might range from $2 \%$ to $5 \%$. One common strategy for predicting rare events is to build a model on a sample consisting of all the events and then merge to a sample of the nonevents. The values of some business targets might not relate to the event of interest or to the business goal. For example, the rare event could be a churn event, when the customer decides to quit the company or the product or service. The nonevent is when the customer does not make churn, and when the customer is still in the company, consuming and using some of the products and services. The new sample might have a $50 \%$ event rate instead of the original $2 \%$ to $5 \%$ event rate.
The advantage of event-based sampling is that data scientists can obtain, on average, a model of similar predictive power with a smaller overall case count. This sampling strategy works because the amount of information in a data set with a categorical outcome is determined not by the total number of observations in the data set, but by the number of observations in the rarest outcome category, which is usually the number of events. This sampling approach allows the model to capture both relationships between the inputs and the event of interest and the inputs and the nonevent. If you have in the training data set just nonevents, or at least a vast majority of them, the model tends to easily capture just the relationship of the inputs and the nonevent. Even if the model predicts there is no churn, the model will be correct $99 \%$ of the time. This $99 \%$ classification rate for this model is a very good classification rate, but the company will miss all the events. The overall model performance will be good, but the company will miss the opportunity to retain possible churners.

统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|Partitioning

Analytical models tend to learn very fast and efficiently capture the relationship between the input variables and target. The problem is they can learn too much. The models can capture almost perfectly the correlation between the inputs and the target, but just for the time frame that it has been trained. As the data changes over time, the model should generalize as much as possible to account for the variability of the data in different time frames. When the model has high predictive accuracy for the trained period but not for future data, it is said that the model overfits the data.

The simplest strategy for correcting overfitting is to isolate a portion of the data for assessment or validation. The model is fit to one part of the data, called the training data set, and the performance is evaluated on another part of the data, called the validation data set. When the validation data are used for comparing, selecting, and modifying models, and the chosen model is assessed on the same data that was used for comparison, then the overfitting principle still applies. In this situation, a test data set should be used for a final assessment.
In situations where there is a time component, the test data set could be gathered from a different time. This would generalize the model even more, as the model should be deployed in production in a different time frame anyway. That means a model is trained based on past events where the target is known. Then once the best model is selected, it will be deployed in production to predict the events in future data, where the target is unknown. The model needs to generalize well to new data to account for variability in the data over time. When a test is made by using data varying in time, it helps the data scientist select the most accurate model but also the one that generalizes best. For example, a model that is fit on data that is gathered from January to June might not generalize well to data that was gathered from July to December. This also raises a problem that there is no model that lasts forever. In some point in time, the model performance will decay especially if the data changes over time. As customers change their behavior, the data used to describe that behavior will change and the model trained and deployed using past data will no longer accurately predict future events.

统计与机器学习代考

.

.

有限元方法代写

assignmentutor™作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师